Connecting high-resolution 3D chromatin organization with epigenomics.
Connecting high-resolution 3D chromatin organization with epigenomics.
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DOI：
10.1038/s41467-022-29695-6
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发表时间：
2022-04-19
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
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作者：
染色质构象捕捉技术的分辨率不断提高，最近的核小体分辨率染色质接触图使我们能够探索人类细胞中精细的3D染色质组织与表观基因组状态之间的关系。使用公开可用的Micro-C数据集，我们开发了一个深度学习模型CAESAR，以学习从表观基因组特征到3D染色质组织的映射函数。该模型准确地预测了Hi-C未能检测到的精细结构，如短程染色质环和条纹。利用ENCODE和Roadmap表观基因组学项目的现有表观基因组数据集，我们成功地为91个人类组织和细胞系绘制了高分辨率的3D染色质接触图。在输入的高分辨率接触图中，我们确定了基因与其实验验证的调控元件之间的空间相互作用，展示了凯撒在高分辨率下将转录调控与3D染色质组织相结合的潜力。虽然大规模的3D基因组结构得到了很好的研究，但分辨率的限制阻碍了我们在精细尺度上的理解。在这里，作者用他们的深度学习模型Caesar将1D表观基因组图谱映射到精细的3D染色质结构。该模型预测了Hi-C数据集无法检测到的精细结构，如短程染色质环和条纹。
The resolution of chromatin conformation capture technologies keeps increasing, and the recent nucleosome resolution chromatin contact maps allow us to explore how fine-scale 3D chromatin organization is related to epigenomic states in human cells. Using publicly available Micro-C datasets, we develop a deep learning model, CAESAR, to learn a mapping function from epigenomic features to 3D chromatin organization. The model accurately predicts fine-scale structures, such as short-range chromatin loops and stripes, that Hi-C fails to detect. With existing epigenomic datasets from ENCODE and Roadmap Epigenomics Project, we successfully impute high-resolution 3D chromatin contact maps for 91 human tissues and cell lines. In the imputed high-resolution contact maps, we identify the spatial interactions between genes and their experimentally validated regulatory elements, demonstrating CAESAR’s potential in coupling transcriptional regulation with 3D chromatin organization at high resolution. While large-scale 3D genome architecture is well studied, the limits of resolution have hindered our understanding on the fine scale. Here the authors mapped 1D epigenomic profiles to fine-scale 3D chromatin structures with their deep learning model CAESAR. The model predicted fine-scale structures, such as short-range chromatin loops and stripes, that Hi-C datasets fail to detect.
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